Data Science + GenAI

Month 1 – Python for Data Science & Analytical Thinking

Objective: Build strong Python programming and analytical foundations.

Week 1: Python Essentials

• Variables, functions, control flow

• Loops, list comprehensions

• Jupyter Notebooks, virtual environments

• Deliverables: Calculator, CSV Parser, File Organizer

Week 2: NumPy Foundations

• Arrays and broadcasting

• Vectorized operations

• Numerical computing and memory efficiency

• Deliverables: NumPy Lab Notebook, Array Transformations Project

Week 3: Pandas for Data Manipulation

• Series and DataFrames

• Grouping, merging, indexing

• Missing values and categorical data handling

• Deliverables: Movie Ratings EDA Project

Week 4: Data Visualization & Storytelling

• Matplotlib, Seaborn, Plotly

• Data storytelling using charts

• Capstone: EDA on a Kaggle Dataset

Month Outcome:

• Strong data wrangling skills

• Complete EDA project on GitHub

Month 2 – Statistics, Probability & Data Preprocessing

Objective: Build statistical intuition and master data cleaning.

Week 5: Statistics for Data Science

• Mean, median, variance

• Correlation and covariance

• Sampling, distributions, skewness, kurtosis

• Deliverables: Statistical Summary Report (Sales Dataset)

Week 6: Probability & Hypothesis Testing

• Bayes Theorem

• Z-test, T-test, Chi-square, ANOVA

• Confidence intervals and p-values

• Deliverables: A/B Testing Case Study

Week 7: Data Preprocessing & Feature Engineering

• Outlier detection

• Encoding and scaling

• Feature transformations

• Deliverables: Feature Engineering Project (Loan Dataset)

Week 8: EDA Integration Project

• Cleaning and preprocessing

• Visualization and insights

• Capstone: Streamlit Data Insights Dashboard

Month Outcome:

• Strong EDA, visualization, and statistical reasoning skills

Month 3 – Machine Learning Foundations

Objective: Learn and apply core ML algorithms end-to-end.

Week 9: Regression Models

• Linear, Ridge, Lasso, Polynomial Regression

• Metrics: RMSE, MAE, R²

• Deliverables: House Price Prediction Project

Week 10: Classification Algorithms

• Logistic Regression

• Decision Trees, Random Forest

• Evaluation metrics and ROC

• Deliverables: Email Spam Classifier

Week 11: Model Evaluation & Validation

• Train-test split

• Cross-validation

• GridSearchCV

• Deliverables: Model Comparison Report

Week 12: End-to-End ML Workflow

• Data ingestion

• Training and testing pipelines

• Capstone: Customer Churn Prediction

Month Outcome:

• Complete mastery of ML workflow

Month 4 – Advanced Machine Learning & Deployment

Objective: Learn advanced ML techniques and deployment.

Week 13: Ensemble Learning

• Bagging and Boosting

• XGBoost, LightGBM

• Deliverables: Credit Risk Classification Project

Week 14: Model Explainability

• SHAP and LIME

• Feature importance visualization

• Deliverables: Explainable AI Analysis Notebook

Week 15: Dimensionality Reduction

• PCA, t-SNE, UMAP

• Intro to autoencoders

• Deliverables: Image Dataset Compression using PCA

Week 16: Model Deployment & Dashboards

• Streamlit, Flask, Gradio

• Capstone: Loan Default Prediction Web App

Month Outcome:

• Advanced ML, explainability, and deployment skills

Month 5 – Deep Learning with TensorFlow & PyTorch

Objective: Build and deploy neural networks.

Week 17: Neural Network Fundamentals

• Perceptron

• Activation functions

• Forward and backward propagation

• Deliverables: Neural Network from Scratch (NumPy)

Week 18: TensorFlow & Keras

• Sequential API

• Optimizers and callbacks

• Deliverables: MNIST Digit Classifier

Week 19: CNNs & Transfer Learning

• CNN architecture

• VGG and ResNet fine-tuning

• Deliverables: Image Classifier using Transfer Learning

Week 20: PyTorch & GPU Acceleration

• PyTorch workflow

• DataLoaders and autograd

• GPU training

• Capstone: Facial Emotion Recognition (Deployed)

Month Outcome:

• Hands-on deep learning and deployment experience

Month 6 – NLP & LLM Integration

Objective: Build NLP systems and integrate LLMs.

Week 21: NLP Fundamentals

• Tokenization, stemming, lemmatization

• Bag of Words, TF-IDF

• Deliverables: Sentiment Analysis (IMDb Dataset)

Week 22: Word Embeddings & RNNs

• Word2Vec

• LSTM and GRU

• Deliverables: Text Generator Project

Week 23: Transformers & BERT

• Attention mechanism

• Fine-tuning BERT

• Deliverables: BERT-based Sentiment Classifier

Week 24: LLM Integration & LangChain

• OpenAI API

• LangChain and embeddings

• Prompt engineering

• Capstone: Resume Reviewer AI App

Month Outcome:

• NLP pipelines and LLM integration expertise

Month 7 – Data Engineering, Cloud & MLOps

Objective: Build scalable pipelines and production systems.

Week 25: Data Pipelines & ETL

• Airflow and Luigi

• Batch vs streaming

• Deliverables: Automated ETL Pipeline

Week 26: Databases & Warehousing

• SQL optimization

• NoSQL basics

• BigQuery and Redshift

• Deliverables: Data Warehouse Simulation

Week 27: MLOps Foundations

• MLflow

• DVC

• Model versioning

• Deliverables: MLflow Model Tracking Project

Week 28: Cloud Deployment

• Docker

• Kubernetes

• CI/CD pipelines

• Capstone: Predictive Maintenance ML Pipeline

Month Outcome:

• MLOps and cloud deployment readiness

Month 8 – Final Capstone & Career Launch

Objective: Become fully job-ready.

Week 29: Ideation & Planning

• Project scope definition

• Dataset selection

• Architecture design

Week 30: Model Development

• Model building and tuning

• Testing and iteration

Week 31: Integration & Deployment

• Dashboard creation

• LLM integration

• Final deployment

Week 32: Career Readiness & Demo

• Resume review

• GitHub and LinkedIn optimization

• Mock interviews (DSA, ML, System Design)

Final Capstone Options

• AI-Powered Recommendation Engine

• Document Summarizer using LLMs

• Predictive Business Intelligence Dashboard

Month Outcome:

• Fully deployed AI project

• Career-ready portfolio

Job Readiness Track (Parallel)

• DSA & Coding Practice: 2 problems per day (200+ solved problems)

• Resume & Portfolio: Month 3 and Month 8

• Mock Interviews: Month 7–8 (3 Technical + 1 HR)

• Hackathons: Every 2 months (2+ submissions)

• Open Source Contributions: Month 5–7

Final Outcome

• 8 Full Projects (6 ML + 2 AI Capstones)

• Strong mastery in ML, AI, and MLOps

• Cloud-ready, interview-ready, job-ready profile